Sounds carry a lot of information in our everyday environment. Be it some footsteps outside your door, laughter from another room, a fire alarm, or incessant barking of a dog on the street, every audio event conveys a certain meaning and may have an important impact on its surroundings. Fortunately, we humans can usually recognize most of such events and act accordingly. However, the development of signal processing and machine learning methods to detect events embedded in acoustic signals is still in its nascent stages. Moreover, most techniques until now have focused on isolated sound events (monophonic) in noise-free synthetic environments whereas in reality most sound events occur in noisy conditions at overlapping time intervals (polyphonic). The latter, therefore makes this field of Acoustic Event Detection (AED) both a challenging and an interesting area of research.